bunch of plumbing of bias all around. measuring bias=False to be about 6% faster

bench.py

@@ -11,6 +11,7 @@ from model import GPTConfig, GPT
 # -----------------------------------------------------------------------------
 batch_size = 8
 block_size = 1024
+bias = True
 seed = 1337
 device = 'cuda' # examples: 'cpu', 'cuda', 'cuda:0', 'cuda:1', etc.
 dtype = 'bfloat16' # 'float32' or 'bfloat16' or 'float16'
@@ -50,6 +51,7 @@ gptconf = GPTConfig(
     block_size = block_size, # how far back does the model look? i.e. context size
     n_layer = 12, n_head = 12, n_embd = 768, # size of the model
     dropout = 0, # for determinism
+    bias = bias,
 )
 model = GPT(gptconf)
 model.to(device)

model.py

@@ -108,7 +108,7 @@ class GPTConfig:
     n_layer: int = 12
     n_head: int = 12
     n_embd: int = 768
-    dropout: float = 0.1
+    dropout: float = 0.0
     bias: bool = True # True: bias in Linears and LayerNorms, like GPT-2. False: a bit better and faster
 
 class GPT(nn.Module):
@@ -215,7 +215,7 @@ class GPT(nn.Module):
         # later, by calling crop_block_size()
 
         # create a from-scratch initialized minGPT model
-        config = GPTConfig(block_size=1024, **config_args)
+        config = GPTConfig(block_size=1024, bias=True, **config_args) # note: force bias=True, as in gpt2 models
         model = GPT(config)
         sd = model.state_dict()
 

train.py

@@ -53,6 +53,7 @@ n_layer = 12
 n_head = 12
 n_embd = 768
 dropout = 0.0 # for pretraining 0 is good, for finetuning try 0.1+
+bias = False # do we use bias inside LayerNorm and Linear layers?
 # adamw optimizer
 learning_rate = 6e-4 # max learning rate
 max_iters = 600000 # total number of training iterations
@@ -129,7 +130,8 @@ else:
     vocab_size = 50257
 
 # model init
-model_args = dict(n_layer = n_layer, n_head = n_head, n_embd = n_embd, block_size = block_size, dropout = dropout, vocab_size = vocab_size)
+model_args = dict(n_layer=n_layer, n_head=n_head, n_embd=n_embd, block_size=block_size,
+                  dropout=dropout, vocab_size=vocab_size, bias=bias)
 if init_from == 'scratch':
     # init a new model from scratch
     print("Initializing a new model from scratch")
@@ -158,6 +160,7 @@ elif init_from == 'resume':
     best_val_loss = checkpoint['best_val_loss']
 elif init_from.startswith('gpt2'):
     print(f"Initializing from OpenAI GPT-2 weights: {init_from}")
+    assert bias, "GPT-2 models have bias, so we can't use bias=False"
     # initialize from OpenAI GPT-2 weights
     override_args = dict(dropout=dropout)
     model = GPT.from_pretrained(init_from, override_args)